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      S-carboxymethylcysteine in the treatment of glue ear: quantitative systematic review

      research-article
      1 , , 2 , 2 , 3
      BMC Family Practice
      BioMed Central

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          Abstract

          Objective

          To establish the clinical relevance of S-carboxymethylcysteine in the treatment of glue ear in children using measures approximating those saving a child from operation for grommet insertion.

          Data sources

          Cochrane Library, MEDLINE, EMBASE, PubMed, reference lists and reviews were used for randomised controlled trials comparing S-carboxymethylcysteine with placebo. Seven trials involving 283 children and 146 ears were found.

          Review methods

          Studies were randomised, double-blind comparisons of S-carboxymethylcysteine (any dose and duration) with placebo in otitis media with effusion. Quality of trial reporting and validity of methods were assessed and used in sensitivity analysis. Main outcomes were relative benefit and number-needed-to-treat to prevent one grommet operation compared with placebo.

          Results

          Successful outcomes were obtained in 17% of children given placebo (range 5% to 38% in individual studies) and in 35% of children given S-carboxymethylcysteine (range 22 to 80%). For combined data (children and ears) the relative benefit was 2.0 (95%CI 1.4 to 2.8) and number-needed-to-treat 5.5 (95% confidence interval 3.8 to 9.8). Pooled data from trials of higher reporting quality (4/7) or methodological validity (3/7) tended to have lower efficacy but were not statistically different from those of lower quality or validity.

          Conclusion

          S-carboxymethylcysteine is effective in the treatment of children with glue ear. For every five or six children treated with S-carboxymethylcysteine over one to three months, one will not undergo surgery for grommet insertion who would have done had they been given placebo. The confidence in this conclusion is limited because studies included relatively few children.

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          Most cited references36

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          Meta-analysis in clinical research.

          Meta-analysis is the process of combining study results that can be used to draw conclusions about therapeutic effectiveness or to plan new studies. We review important design and statistical issues of this process. The design issues include protocol development, objectives, literature search, publication bias, measures of study outcomes, and quality of the data. The statistical issues include consistency (homogeneity) of study outcomes, and techniques for pooling results from several studies. Guidelines are provided to assess the quality of meta-analyses based on our discussion of the design and statistical issues. Limitations and areas for further development of this approach are discussed; researchers should come to a general agreement on how to conduct meta-analysis. As an explicit strategy for summarizing results, meta-analysis may help clinicians and researchers better understand the findings of clinical studies.
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            Size is everything--large amounts of information are needed to overcome random effects in estimating direction and magnitude of treatment effects.

            Variability in patients' response to interventions in pain and other clinical settings is large. Many explanations such as trial methods, environment or culture have been proposed, but this paper sets out to show that the main cause of the variability may be random chance, and that if trials are small their estimate of magnitude of effect may be incorrect, simply because of the random play of chance. This is highly relevant to the questions of 'How large do trials have to be for statistical accuracy?' and 'How large do trials have to be for their results to be clinically valid?' The true underlying control event rate (CER) and experimental event rate (EER) were determined from single-dose acute pain analgesic trials in over 5000 patients. Trial group size required to obtain statistically significant and clinically relevant (0.95 probability of number-needed-to-treat within -/+0.5 of its true value) results were computed using these values. Ten thousand trials using these CER and EER values were simulated using varying group sizes to investigate the variation due to random chance alone. Most common analgesics have EERs in the range 0.4-0.6 and CER of about 0.19. With such efficacy, to have a 90% chance of obtaining a statistically significant result in the correct direction requires group sizes in the range 30-60. For clinical relevance nearly 500 patients are required in each group. Only with an extremely effective drug (EER > 0.8) will we be reasonably sure of obtaining a clinically relevant NNT with commonly used group sizes of around 40 patients per treatment arm. The simulated trials showed substantial variation in CER and EER, with the probability of obtaining the correct values improving as group size increased. We contend that much of the variability in control and experimental event rates is due to random chance alone. Single small trials are unlikely to be correct. If we want to be sure of getting correct (clinically relevant) results in clinical trials we must study more patients. Credible estimates of clinical efficacy are only likely to come from large trials or from pooling multiple trials of conventional (small) size.
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              Confidence intervals rather than P values: estimation rather than hypothesis testing.

              Overemphasis on hypothesis testing--and the use of P values to dichotomise significant or non-significant results--has detracted from more useful approaches to interpreting study results, such as estimation and confidence intervals. In medical studies investigators are usually interested in determining the size of difference of a measured outcome between groups, rather than a simple indication of whether or not it is statistically significant. Confidence intervals present a range of values, on the basis of the sample data, in which the population value for such a difference may lie. Some methods of calculating confidence intervals for means and differences between means are given, with similar information for proportions. The paper also gives suggestions for graphical display. Confidence intervals, if appropriate to the type of study, should be used for major findings in both the main text of a paper and its abstract.
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                Author and article information

                Journal
                BMC Fam Pract
                BMC Family Practice
                BioMed Central (London )
                1471-2296
                2001
                12 September 2001
                : 2
                : 3
                Affiliations
                [1 ]Pain Research and Nuffield Department of Anaesthetics, University of Oxford, Oxford Radcliffe Hospitals, The Churchill Headington, Oxford, OX3 7LJ, UK
                [2 ]ENT Department, Radcliffe Infirmary, Oxford, OX2 6HE, UK
                [3 ]School of Health Sciences, University of Wales, Swansea Singleton Park, Swansea, SA2 8PP, UK
                Article
                1471-2296-2-3
                10.1186/1471-2296-2-3
                57002
                11580867
                ded95966-1ce9-48ab-b309-dede0754cefc
                Copyright © 2001 Moore et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
                History
                : 10 October 2000
                : 12 September 2001
                Categories
                Research Article

                Medicine
                Medicine

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